Influence of Behavioral and Psychological Symptoms on Caregiver Burden for Different Types of Dementia: Clinical Experience in Lima, Peru.

INTRODUCTION: People caring for patients with dementia are prone to suffering from burden. Behavioral and psychological symptoms of dementia (BPSD) may have an impact on caregiver burden. In Latin American countries, there is a lack of research on caregiver burden. We aimed to determine which BPSD have the greatest impact on caregiver burden among Peruvian patients with dementia and to compare the effects of BPSD on caregiver burden across different types of dementia. METHODS: A cross-sectional study was conducted on 231 patients living with Alzheimer's dementia (AD), behavioral variant frontotemporal dementia (bvFTD), dementia with Lewy bodies (DLB), and vascular dementia (VD) and their caregivers who attended a Peruvian memory clinic. BPSD were assessed with the Neuropsychiatric Inventory (NPI). Caregiver burden was assessed with the Zarit Burden Inventory. We used analysis of variance to compare the AD, bvFTD, DLB, and VD groups. Correlations between Zarit Burden Inventory and NPI subscale scores were assessed with Spearman's correlation. RESULTS: DLB caregivers had significantly higher levels of burden than the other patient groups (p < 0.05) and higher total NPI scores than caregivers for other patient groups (p < 0.05). bvFTD caregivers had significantly higher total NPI scores than AD and VD caregivers (p < 0.05). Hallucinations, aberrant motor behavior, and apathy were the symptoms most significantly correlated with caregiver burden in those caring for DLB, bvFTD, and AD patients, respectively. CONCLUSION: Neuropsychiatric symptoms are higher in DLB caregivers. Hallucinations, aberrant motor behavior, and apathy are the main symptoms correlated with burden.

Efficacy and safety of biological treatment for inflammatory bowel disease in elderly patients: Results from a GETECCU cohort.

INTRODUCTION: Biological therapies used for the treatment of inflammatory bowel disease (IBD) have shown to be effective and safe, although these results were obtained from studies involving mostly a young population, who are generally included in clinical trials. The aim of our study was to determine the efficacy and safety of the different biological treatments in the elderly population. METHODS: Multicenter study was carried out in the GETECCU group. Patients diagnosed with IBD and aged over 65 years at the time of initiating biological therapy (infliximab, adalimumab, golimumab, ustekinumab or vedolizumab) were retrospectively included. Among the patients included, clinical response was assessed after drug induction (12 weeks of treatment) and at 52 weeks. Patients' colonoscopy data in week 52 were assessment, where available. Regarding complications, development of oncological events during follow-up and infectious processes occurring during biological treatment were collected (excluding bowel infection by cytomegalovirus). RESULTS: A total of 1090 patients were included. After induction, at approximately 12-14 weeks of treatment, 419 patients (39.6%) were in clinical remission, 502 patients (47.4%) had responded without remission and 137 patients (12.9%) had no response. At 52 weeks of treatment 442 patients (57.1%) had achieved clinical remission, 249 patients had responded without remission (32.2%) and 53 patients had no response to the treatment (6.8%). Before 52 weeks, 129 patients (14.8%) had discontinued treatment due to inefficacy, this being significantly higher (p<0.0001) for Golimumab - 9 patients (37.5%) - compared to the other biological treatments analyzed. With respect to tumor development, an oncological event was observed in 74 patients (6.9%): 30 patients (8%) on infliximab, 23 (7.14%) on adalimumab, 3 (11.1%) on golimumab, 10 (6.4%) on ustekinumab, and 8 (3.8%) on vedolizumab. The incidence was significantly lower (p=0.04) for the vedolizumab group compared to other treatments. As regards infections, these occurred in 160 patients during treatment (14.9%), with no differences between the different biologicals used (p=0.61): 61 patients (19.4%) on infliximab, 39 (12.5%) on adalimumab, 5 (17.8%) on golimumab, 22 (14.1%) on ustekinumab, and 34 (16.5%) on vedolizumab. CONCLUSIONS: Biological drug therapies have response rates in elderly patients similar to those described in the general population, Golimumab was the drug that was discontinued most frequently due to inefficacy. In our experience, tumor development was more frequent in patients who used anti-TNF therapies compared to other targets, although its incidence was generally low and that this is in line with younger patients based on previous literature.

Zoonotic Transmission of Hepatic Hydatid Cyst from Domestic Dogs: A Case Report from an Urban-Marginal Area in Ecuador.

BACKGROUND Hepatic hydatidosis, or echinococcosis, is a zoonosis with worldwide prevalence and is potentially lethal in humans. This report presents a case of hydatidosis in a 40-year-old woman that was associated with a zoonotic transmission of Echinococcus granulosus from domestic dogs in an urban-marginal area of the city of Guayaquil, Ecuador. This report shows how early diagnosis and awareness favored the correct treatment of the disease. CASE REPORT A 40-year-old woman from the aforementioned sector presented the following symptoms: malaise, pain in the upper right hypochondrium, palpable mass, jaundice, and fever. Next, the patient's fecal samples were analyzed by direct coproparasitic methods, flotation, and sedimentation with centrifugation using saline solution, whereby the presence of Entamoeba histolytica eggs was determined. Likewise, she underwent an ultrasound, in which hepatic hydatid cysts were observed. Subsequently, the cysts were treated and surgically removed, and parasitic forms of E. granulosus were identified. Later, coproparasitic analysis of her 2 domestic dogs for coproantigen ELISA were performed, by which the presence of this cestode was also identified and confirmed. CONCLUSIONS Hydatidosis is a zoonosis that can affect the population, especially in endemic areas of developed and underdeveloped countries. In this case, hepatic hydatidosis was identified in a 40-year-old woman. Additionally, the presence of E. granulosus eggs was determined in the fecal matter of her dogs, which indicated that the patient's relatives and other people around them were exposed to this zoonosis.

Cybersecurity Analysis of Wearable Devices: Smartwatches Passive Attack.

Wearable devices are starting to gain popularity, which means that a large portion of the population is starting to acquire these products. This kind of technology comes with a lot of advantages, as it simplifies different tasks people do daily. However, as they recollect sensitive data, they are starting to be targets for cybercriminals. The number of attacks on wearable devices forces manufacturers to improve the security of these devices to protect them. Many vulnerabilities have appeared in communication protocols, specifically Bluetooth. We focus on understanding the Bluetooth protocol and what countermeasures have been applied during their updated versions to solve the most common security problems. We have performed a passive attack on six different smartwatches to discover their vulnerabilities during the pairing process. Furthermore, we have developed a proposal of requirements needed for maximum security of wearable devices, as well as the minimum requirements needed to have a secure pairing process between two devices via Bluetooth.

Síndrome metabólico en el adulto mayor vinculado a los programas de actividad física comunitaria / Metabolic Syndrome in the Elderly Adult Involved in the Community Physical Activity Programs

Introducción: El síndrome metabólico es una situación clínica compleja que se asocia a un incremento de la morbilidad y mortalidad. Los elementos que lo componen aumentan el riesgo de diabetes mellitus tipo II y enfermedad cardiovascular. Objetivo: Determinar el comportamiento del síndrome metabólico en el adulto mayor vinculado a los programas de actividad física comunitaria del proyecto Lindo Amanecer del municipio Arroyo Naranjo. Métodos: Se realizó un estudio observacional descriptivo de corte transversal. El universo de estudio lo constituyeron 120 adultos mayores, de los que se entrevistaron a 106, en el período de abril a octubre de 2018. Se siguieron los criterios del Adult Treatment Panel III para el diagnóstico de síndrome metabólico. Las variables descriptivas se expresaron en porcientos y para la comparación de variables en estudio se utilizó el método estadístico de ji al cuadrado. Resultados: Los resultados obtenidos mostraron un 41,51 por ciento de personas con síndrome metabólico, predominaron las personas de 70 y más años de edad (54,54 por ciento y el sexo femenino (93,18 por ciento). El 100 por ciento tuvieron cifras de presión arterial ≥ 130/85 mmHg. El 100 por ciento de los pacientes desconocían su enfermedad. Conclusiones: Se encontró predominio en los pacientes con síndrome metabólico del sexo femenino y del grupo de edad de 70 y más años. El diagnóstico a nivel de la Atención Primaria de Salud es deficiente. Se asocia a la hipertensión arterial, obesidad abdominal y al riesgo de enfermedad cardiovascular(AU)

Introduction: Metabolic syndrome is a complex clinical situation associated with an increase in morbidity and mortality. The elements that mark it up increase the risk of type 2 diabetes mellitus and cardiovascular disease. Objective: To determine the behavior of metabolic syndrome in elderly adults involved in the community physical activity programs of the Lindo Amanecer project in the municipality of Arroyo Naranjo. Methods: A cross-sectional descriptive observational study was carried out. The study universe was made up of 120 elderly adults, 106 of which were interviewed in the period from April to October 2018. The Adult Treatment Panel III criteria for the diagnosis of metabolic syndrome were followed. The descriptive variables were expressed in percentages and, for the comparison of variables under study, the chi-square statistical method was used. Results: The obtained results showed 41.51percent of people with metabolic syndrome, with a predominance of people aged 70 years and older (54.54percent) and the female sex (93.18percent). One hundred percent had blood pressure values over or equal to 130/85 mmHg. One hundred percent of the patients did not have any knowledge of their disease. Conclusions: In patients with metabolic syndrome, the predominance corresponded to the female sex, as well as the age group of 70 years and older. Diagnosis at the primary healthcare level is deficient. It is associated with arterial hypertension, abdominal obesity and the risk of cardiovascular disease(AU)

ReinforSec: An Automatic Generator of Synthetic Malware Samples and Denial-of-Service Attacks through Reinforcement Learning.

In recent years, cybersecurity has been strengthened through the adoption of processes, mechanisms and rapid sources of indicators of compromise in critical areas. Among the most latent challenges are the detection, classification and eradication of malware and Denial of Service Cyber-Attacks (DoS). The literature has presented different ways to obtain and evaluate malware- and DoS-cyber-attack-related instances, either from a technical point of view or by offering ready-to-use datasets. However, acquiring fresh, up-to-date samples requires an arduous process of exploration, sandbox configuration and mass storage, which may ultimately result in an unbalanced or under-represented set. Synthetic sample generation has shown that the cost associated with setting up controlled environments and time spent on sample evaluation can be reduced. Nevertheless, the process is performed when the observations already belong to a characterized set, totally detached from a real environment. In order to solve the aforementioned, this work proposes a methodology for the generation of synthetic samples of malicious Portable Executable binaries and DoS cyber-attacks. The task is performed via a Reinforcement Learning engine, which learns from a baseline of different malware families and DoS cyber-attack network properties, resulting in new, mutated and highly functional samples. Experimental results demonstrate the high adaptability of the outputs as new input datasets for different Machine Learning algorithms.

On Detecting Cryptojacking on Websites: Revisiting the Use of Classifiers.

Cryptojacking or illegal mining is a form of malware that hides in the victim's computer and takes the computational resources to extract cryptocurrencies in favor of the attacker. It generates significant computational consumption, reducing the computational efficiency of the victim's computer. This attack has increased due to the rise of cryptocurrencies and their profitability and its difficult detection by the user. The identification and blocking of this type of malware have become an aspect of research related to cryptocurrencies and blockchain technology; in the literature, some machine learning and deep learning techniques are presented, but they are still susceptible to improvement. In this work, we explore multiple Machine Learning classification models for detecting cryptojacking on websites, such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting Classifier, k-Nearest Neighbor, and XGBoost. To this end, we make use of a dataset, composed of network and host features' samples, to which we apply various feature selection methods such as those based on statistical methods, e.g., Test Anova, and other methods as Wrappers, not only to reduce the complexity of the built models but also to discover the features with the greatest predictive power. Our results suggest that simple models such as Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and k-Nearest Neighbor models, can achieve success rate similar to or greater than that of advanced algorithms such as XGBoost and even those of other works based on Deep Learning.

Differing Escape Responses of the Marine Bacterium Marinobacter adhaerens in the Presence of Planktonic vs. Surface-Associated Protist Grazers.

Protist grazing pressure plays a major role in controlling aquatic bacterial populations, affecting energy flow through the microbial loop and biogeochemical cycles. Predator-escape mechanisms might play a crucial role in energy flow through the microbial loop, but are yet understudied. For example, some bacteria can use planktonic as well as surface-associated habitats, providing a potential escape mechanism to habitat-specific grazers. We investigated the escape response of the marine bacterium Marinobacter adhaerens in the presence of either planktonic (nanoflagellate: Cafeteria roenbergensis) or surface-associated (amoeba: Vannella anglica) protist predators, following population dynamics over time. In the presence of V. anglica, M. adhaerens cell density increased in the water, but decreased on solid surfaces, indicating an escape response towards the planktonic habitat. In contrast, the planktonic predator C. roenbergensis induced bacterial escape to the surface habitat. While C. roenbergensis cell numbers dropped substantially after a sharp initial increase, V. anglica exhibited a slow, but constant growth throughout the entire experiment. In the presence of C. roenbergensis, M. adhaerens rapidly formed cell clumps in the water habitat, which likely prevented consumption of the planktonic M. adhaerens by the flagellate, resulting in a strong decline in the predator population. Our results indicate an active escape of M. adhaerens via phenotypic plasticity (i.e., behavioral and morphological changes) against predator ingestion. This study highlights the potentially important role of behavioral escape mechanisms for community composition and energy flow in pelagic environments, especially with globally rising particle loads in aquatic systems through human activities and extreme weather events.

FASSVid: Fast and Accurate Semantic Segmentation for Video Sequences.

Most of the methods for real-time semantic segmentation do not take into account temporal information when working with video sequences. This is counter-intuitive in real-world scenarios where the main application of such methods is, precisely, being able to process frame sequences as quickly and accurately as possible. In this paper, we address this problem by exploiting the temporal information provided by previous frames of the video stream. Our method leverages a previous input frame as well as the previous output of the network to enhance the prediction accuracy of the current input frame. We develop a module that obtains feature maps rich in change information. Additionally, we incorporate the previous output of the network into all the decoder stages as a way of increasing the attention given to relevant features. Finally, to properly train and evaluate our methods, we introduce CityscapesVid, a dataset specifically designed to benchmark semantic video segmentation networks. Our proposed network, entitled FASSVid improves the mIoU accuracy performance over a standard non-sequential baseline model. Moreover, FASSVid obtains state-of-the-art inference speed and competitive mIoU results compared to other state-of-the-art lightweight networks, with significantly lower number of computations. Specifically, we obtain 71% of mIoU in our CityscapesVid dataset, running at 114.9 FPS on a single NVIDIA GTX 1080Ti and 31 FPS on the NVIDIA Jetson Nano embedded board with images of size 1024×2048 and 512×1024, respectively.

Critical Analysis of Hypothesis Tests in Federal Information Processing Standard (140-2).

This work presents an analysis of the existing dependencies between the tests of the FIPS 140-2 battery. Two main analytical approaches are utilized, the first being a study of correlations through the Pearson's correlation coefficient that detects linear dependencies, and the second one being a novel application of the mutual information measure that allows detecting possible non-linear relationships. In order to carry out this study, the FIPS 140-2 battery is reimplemented to allow the user to obtain p-values and statistics that are essential for more rigorous end-user analysis of random number generators (RNG).

Riesgo cardiovascular global y obesidad en pacientes del primer nivel de atención / Global cardiovascular risk and obesity in first level care patients

Introducción: La mejor herramienta para priorizar la prevención primaria de enfermedades cardiovasculares es estimar el riesgo cardiovascular global. El riesgo de enfermedades cardiovasculares se incrementa con el aumento de peso corporal. Objetivo: Determinar el riesgo cardiovascular global y relacionarlo con la obesidad en pacientes del primer nivel de atención. Material y Métodos: Se realizó un estudio descriptivo de corte transversal. El universo de estudio consistió en 803 personas; se extrajo una muestra de 585 personas de los tres grupos de estudio entre enero de 2016 y octubre de 2018. El cálculo del riesgo cardiovascular global se realizó según los criterios de las tablas de la Organización Mundial de la Salud. Se relacionó con la circunferencia de la cintura y el índice de masa corporal. Resultados: El 51,28 por ciento de las personas estudiadas presentó un nivel de riesgo cardiovascular global bajo, seguido del nivel de riesgo moderado con 29,92 por ciento y en los niveles de riesgo alto, muy alto y extremadamente alto 18,80 por ciento. El 10,94 por ciento son personas obesas, de las cuales 42,19 por ciento se distribuyó entre los niveles de riesgo alto, muy alto y extremadamente alto. El 21,20 por ciento de las personas estudiadas presentó un riesgo muy elevado, según la clasificación de la circunferencia de la cintura. Conclusiones: Predominó el nivel de riesgo cardiovascular global bajo. A medida que avanza la edad se incrementa el riesgo cardiovascular global, con predominio del sexo masculino. La obesidad tuvo relación con el riesgo cardiovascular global(AU)

Introduction: Estimating global cardiovascular risk is the best tool for prioritizing the primary prevention of cardiovascular diseases. The risk of cardiovascular disease increases with increasing body weight. Objective: To determine the global cardiovascular risk and to relate it to obesity in first level care patients. Material and Methods: A descriptive cross-sectional study was carried out. The study universe consisted of 803 people; a sample of 585 people was drawn from the three study groups between January 2016 and October 2018. The calculation of global cardiovascular risk was performed according to the criteria specified in the World Health Organization tables. It was related to waist circumference and body mass index. Results: The results show that 51,28 percent of the people studied presented a low global cardiovascular risk level; followed by a moderate risk level (29,92 percent) and high, very high, and extremely high risk levels (18,80 percent). Similarly, 10,94 percent are obese people, of whom 42,19 percent were distributed between high, very high and extremely high risk levels. Also, 21,20 percent of the people studied presented a very high risk according to the waist circumference classification. Conclusions: A low global cardiovascular risk level predominated in the study. As age advances, the global cardiovascular risk increases, with a predominance of males. Obesity was related to global cardiovascular risk(AU)

A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT.

At present, new data sharing technologies, such as those used in the Internet of Things (IoT) paradigm, are being extensively adopted. For this reason, intelligent security controls have become imperative. According to good practices and security information standards, particularly those regarding security in depth, several defensive layers are required to protect information assets. Within the context of IoT cyber-attacks, it is fundamental to continuously adapt new detection mechanisms for growing IoT threats, specifically for those becoming more sophisticated within mesh networks, such as identity theft and cloning. Therefore, current applications, such as Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and Security Information and Event Management Systems (SIEM), are becoming inadequate for accurately handling novel security incidents, due to their signature-based detection procedures using the matching and flagging of anomalous patterns. This project focuses on a seldom-investigated identity attack-the Clone ID attack-directed at the Routing Protocol for Low Power and Lossy Networks (RPL), the underlying technology for most IoT devices. Hence, a robust Artificial Intelligence-based protection framework is proposed, in order to tackle major identity impersonation attacks, which classical applications are prone to misidentifying. On this basis, unsupervised pre-training techniques are employed to select key characteristics from RPL network samples. Then, a Dense Neural Network (DNN) is trained to maximize deep feature engineering, with the aim of improving classification results to protect against malicious counterfeiting attempts.

Improving Real-Time Hand Gesture Recognition with Semantic Segmentation.

Hand gesture recognition (HGR) takes a central role in human-computer interaction, covering a wide range of applications in the automotive sector, consumer electronics, home automation, and others. In recent years, accurate and efficient deep learning models have been proposed for real-time applications. However, the most accurate approaches tend to employ multiple modalities derived from RGB input frames, such as optical flow. This practice limits real-time performance due to intense extra computational cost. In this paper, we avoid the optical flow computation by proposing a real-time hand gesture recognition method based on RGB frames combined with hand segmentation masks. We employ a light-weight semantic segmentation method (FASSD-Net) to boost the accuracy of two efficient HGR methods: Temporal Segment Networks (TSN) and Temporal Shift Modules (TSM). We demonstrate the efficiency of the proposal on our IPN Hand dataset, which includes thirteen different gestures focused on interaction with touchless screens. The experimental results show that our approach significantly overcomes the accuracy of the original TSN and TSM algorithms by keeping real-time performance.

Stay Connected and Up To Date: GI Meetings and Seminars and the Coronavirus Disease 2019 Pandemic.

The novel coronavirus disease 2019 has become an unprecedented global health and societal problem. The rapid spreading of this pandemic, together with the absence of effective treatments or vaccines, has led governments around the world to take drastic actions, including limiting the free movement of citizens, establishing maximum capacities and enforcing social distancing measures, among others. Consequently, major medical congresses, meetings, conferences, and workshops have been either cancelled or delayed. This situation has forced rapid changes in how the medical and scientific communities interact, and they now mainly meet using online technology. While political leaders have closed the borders of their countries as a logical public health measure, we have witnessed medical societies, clinicians, and scientists breaking other barriers and creating a new reality of global technological collaboration unlike anything seen before. An immense variety of webinars, online conferences, podcasts, courses, and many other forms of uploaded content can be seen from the comfort of one's own workplace or home at any time, and this has exponentially increased our reach; however, this great number of possibilities can be overwhelming, and it is more important than ever to separate wheat from chaff when deciding what online event to attend. The purpose of this article is to describe the impact of coronavirus disease 2019 on congresses and meetings, to examine how GI physicians feel about the emerging trend of online initiatives and to offer suggestions on how to balance web-based meetings and daily work.

An Analysis of Smart Contracts Security Threats Alongside Existing Solutions.

Smart contracts have gained a lot of popularity in recent times as they are a very powerful tool for the development of decentralised and automatic applications in many fields without the need for intermediaries or trusted third parties. However, due to the decentralised nature of the blockchain on which they are based, a series of challenges have emerged related to vulnerabilities in their programming that, given their particularities, could have (and have already had) a very high economic impact. This article provides a holistic view of security challenges associated with smart contracts, as well as the state of the art of available public domain tools.

Anonymous Real-Time Analytics Monitoring Solution for Decision Making Supported by Sentiment Analysis.

Currently, social networks present information of great relevance to various government agencies and different types of companies, which need knowledge insights for their business strategies. From this point of view, an important technique for data analysis is to create and maintain an environment for collecting data and transforming them into intelligence information to enable analysts to observe the evolution of a given topic, elaborate the analysis hypothesis, identify botnets, and generate data to aid in the decision-making process. Focusing on collecting, analyzing, and supporting decision-making, this paper proposes an architecture designed to monitor and perform anonymous real-time searches in tweets to generate information allowing sentiment analysis on a given subject. Therefore, a technological structure and its implementation are defined, followed by processes for data collection and analysis. The results obtained indicate that the proposed solution provides a high capacity to collect, process, search, analyze, and view a large number of tweets in several languages, in real-time, with sentiment analysis capabilities, at a low cost of implementation and operation.

Clinical and Pathological Characterization of Lynch-Like Syndrome.

BACKGROUND & AIMS: Lynch syndrome is characterized by DNA mismatch repair (MMR) deficiency. Some patients with suspected Lynch syndrome have DNA MMR deficiencies but no detectable mutations in genes that encode MMR proteins-this is called Lynch-like syndrome (LLS). There is no consensus on management of patients with LLS. We collected data from a large series of patients with LLS to identify clinical and pathology features. METHODS: We collected data from a nationwide-registry of patients with colorectal cancer (CRC) in Spain. We identified patients whose colorectal tumors had loss of MSH2, MSH6, PMS2, or MLH1 (based on immunohistochemistry), without the mutation encoding V600E in BRAF (detected by real-time PCR), and/or no methylation at MLH1 (determined by methylation-specific multiplex ligation-dependent probe amplification), and no pathogenic mutations in MMR genes, BRAF, or EPCAM (determined by DNA sequencing). These patients were considered to have LLS. We collected data on demographic, clinical, and pathology features and family history of neoplasms. The χ2 test was used to analyze the association between qualitative variables, followed by the Fisher exact test and the Student t test or the Mann-Whitney test for quantitative variables. RESULTS: We identified 160 patients with LLS; their mean age at diagnosis of CRC was 55 years and 66 patients were female (41%). The Amsterdam I and II criteria for Lynch syndrome were fulfilled by 11% of cases and the revised Bethesda guideline criteria by 65% of cases. Of the patients with LLS, 24% were identified in universal screening. There were no proportional differences in sex, indication for colonoscopy, immunohistochemistry, pathology findings, or personal history of CRC or other Lynch syndrome-related tumors between patients who met the Amsterdam and/or Bethesda criteria for Lynch syndrome and patients identified in universal screening for Lynch syndrome, without a family history of CRC. CONCLUSIONS: Patients with LLS have homogeneous clinical, demographic, and pathology characteristics, regardless of family history of CRC.

Secondary colonic necrosis. Case report.

BACKGROUND: Colonic vascular lesion secondary to verapamil overdose is mediated by free radicals, forming vascular microtrombos and endotoxin generation, being a difficult diagnosis. CLINICAL CASE: A 27-year-old female is admitted with an acute abdomen of 4 days after an event referred for a suicidal attempt due to an overdose of verapamil, operating surgically where there is a right transmural colon necrosis, performing a right hemicolectomy with terminal ileostomy. CONCLUSIONS: Recognize and properly treat an acute abdomen, not always reach an adequate diagnosis, so a thorough history could conclude.

ANTECEDENTES: La lesión vascular colónica secundaria a la sobredosis de verapamilo, es mediada por radicales libres, formando microtrombos vasculares y generación de endotoxinas, siendo un diagnostico difícil. CASO CLÍNICO: Femenino de 27 años, ingresa con abdomen agudo de 4 días posteriores a un evento remitido de intento suicida por sobredosis de verapamilo, interviniéndose quirúrgicamente donde se halla necrosis colónica transmural derecha, realizando hemicolectomía derecha con ileostomía terminal. CONCLUSIONES: Reconocer y tratar de forma adecuada un abdomen agudo, no siempre se suele llegar a un adecuado diagnostico, por lo cual una minuciosa anamnesis lograría concluirlo.

Endocuff-assisted colonoscopy for surveillance of serrated polyposis syndrome: a multicenter randomized controlled trial.

BACKGROUND AND STUDY AIMS: Serrated polyposis syndrome (SPS) is a condition with high risk for colorectal cancer. The Endocuff device has been shown to increase adenoma detection in the general and screening population. We aimed to ascertain whether Endocuff-assisted colonoscopy increases detection of serrated lesions in comparison with standard colonoscopy during the surveillance of patients with SPS.  METHODS: In a multicenter randomized controlled study, patients who met SPS criteria I and/or III under surveillance (previous resection of all serrated lesions ≥ 4 mm) were consecutively randomly allocated 1:1 to Endocuff-assisted colonoscopy or standard colonoscopy, performed by expert endoscopists. The main outcome was the mean number of serrated lesions detected per patient. RESULTS: 122 patients (standard colonoscopy n = 60; Endocuff-assisted colonoscopy n = 62; 59 % men; mean age 60.6 (standard deviation [SD] 7.5) were included at 4 centers. Baseline variables (demographic data, SPS phenotype, colorectal cancer [CRC] history, cumulative polyps, and follow-up), cecal intubation rate, and withdrawal time were similar between groups. There was no statistically significant difference between Endocuff-assisted colonoscopy and standard colonoscopy for the mean number of serrated lesions detected per patient: 5.8 (95 % confidence interval [95 %CI] 4.4 - 7.2) and 5.0 (3.9 - 6.1), respectively (P = 0.36). There were also no differences between Endocuff-assisted and standard colonoscopy for detection of sessile serrated lesions (mean number per patient 2.5 [1.3 - 3.6] vs. 2.0 [1.1 - 3.0], P = 0.54) and adenomas (0.9 [0.5 - 1.3] vs. 0.5 [0.3 - 0.7], P = 0.12). CONCLUSION: Use of Endocuff-assisted colonoscopy did not significantly increase the number of serrated lesion detected per patient during surveillance of SPS.

Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach.

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average-dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.